import time

from .results import AgentResult
from .structured_output import build_mock_structured_output
from .tool_registry import run_declared_tools
from .rag.retriever import retrieve


def run_agent(scenario_config: dict, user_input: str, options: dict | None = None) -> AgentResult:
    started_at = time.perf_counter()
    options = options or {}
    output_type = scenario_config.get("output", {}).get("type", "general_answer")

    references = []
    rag_config = scenario_config.get("rag", {})
    if rag_config.get("enabled"):
        references = retrieve(
            scenario_id=scenario_config.get("id", ""),
            query=user_input,
            collection=rag_config.get("collection", scenario_config.get("id", "")),
            top_k=rag_config.get("top_k", 5),
            document_ids=options.get("document_ids"),
            store_path=options.get("rag_store_path"),
        )

    tool_calls = run_declared_tools(scenario_config.get("tools", []), user_input)
    structured_output = build_mock_structured_output(output_type, user_input, references)
    answer = f"已根据「{scenario_config.get('name', '当前场景')}」生成模拟回答：{user_input}"
    latency_ms = int((time.perf_counter() - started_at) * 1000)

    return AgentResult(
        answer=answer,
        structured_output=structured_output,
        references=references,
        tool_calls=tool_calls,
        raw_output=answer,
        model_name=options.get("model_name", "mock-model"),
        latency_ms=latency_ms,
        status="success",
    )